The high polymorphism of histocompatibility complex class Ⅱ (MHC-Ⅱ) alleles and limited immunopeptidomic data hinder pan-species epitope prediction. In this study, leveraging the predictive power of AlphaFold (AF) and the conserved structural features of the core region of MHC-Ⅱ–binding peptides, derived from a comprehensive analysis of MHC-Ⅱ structure data in the PDB database, we developed a new tool, AF-prediction (AF-pred), with explicit quantitative criteria for MHC-Ⅱ–restricted epitope prediction. We validated AF-pred across human, porcine, bovine, and bat MHC-Ⅱ molecules through large-scale in silico analyses using known immunopeptidome datasets (1000 positive and 1000 negative antigenic peptides), together with in vitro binding assays and crystallographic characterization of newly predicted epitopes. Using uncharacterized bat MHC-Ⅱ structures, we demonstrated that AF-pred’s amino-acid interaction prediction underpins its pan-prediction capability and the underlying rationale of the method. Conversely, this characteristic limits the prediction of atypical MHC-Ⅱ peptide-binding modes. Compared with sequence-based tools, AF-pred demonstrates enhanced cross-species MHC-Ⅱ binding prediction, with higher accuracy and interpretability, and further reveals that iterative AF updates improve AF-pred performance. AF-pred has the potential to facilitate the development of novel T-cell epitope vaccines and advance the “One Health” initiative.

Biosynthesis—a process utilizing biological systems to synthesize chemical compounds—has emerged as a revolutionary solution to 21st‐century challenges due to its environmental sustainability, scalability, and high stereoselectivity and regioselectivity. Recent advancements in artificial intelligence (AI) are accelerating biosynthesis by enabling intelligent design, construction, and optimization of enzymatic reactions and biological systems. We first introduce the molecular retrosynthesis route planning in biochemical pathway design, including single‐step retrosynthesis algorithms and AI‐based chemical retrosynthesis route design tools. We highlight the advantages and challenges of large language models in addressing the sparsity of chemical data. Furthermore, we review enzyme discovery methods based on sequence and structure alignment techniques. Breakthroughs in AI‐based structural prediction methods are expected to significantly improve the accuracy of enzyme discovery. We also summarize methods for de novo enzyme generation for nonnatural or orphan reactions, focusing on AI‐based enzyme functional annotation and enzyme discovery techniques based on reaction or small molecule similarity. Turning to enzyme engineering, we discuss strategies to improve enzyme thermostability, solubility, and activity, as well as the applications of AI in these fields. The shift from traditional experiment‐driven models to data‐driven and computationally driven intelligent models is already underway. Finally, we present potential challenges and provide a perspective on future research directions. We envision expanded applications of biocatalysis in drug development, green chemistry, and complex molecule synthesis.